Computational features and linguistic features (TF-IDF, N-gram and 30 best features for LIWC) were used by Shah et al. to propose a hybrid method [1]}. Several features were extracted, including statistical, linguistic, syntactic, topic features, and word embedding. These features were used to detect online suicidal users through their online content [2]}, including the language of the tweet and the emotional historic spectrum feature in a time-aware manner [3]}. Important data that can give information about users on social networks include users’ behavior (daily activities, social network size, etc.) [4]}. Profile, text content such as messages, publications, and comments are used to extract features of a general-purpose classification [5]}. A combination of textual features such as BoW or N-grams and word embedding with social network and psychological features include lexical, behavioral, and sentiment analysis. These and other features can be mapped to social media context using certain signs, symptoms, and image-based features [6]}.
